Data mining of corporate financial fraud based on neural network model

Li, Shenglu

doi:10.18287/2412-6179-co-656

Cited by 10 publications

(4 citation statements)

References 16 publications

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“…In the analysis of financial data, Li constructed financial related indicators and financial report recognition model. The accuracy, precision, recall, and F value show that the performance of the improved BP neural network has been improved [9]. Mwanza and Phiri used intelligent mining algorithm in the research of tax data to realize the outlier algorithm of fraud detection, continuous monitoring based on distance and outlier query based on distance, which improved the accuracy of abnormal data analysis [10].…”

Section: State Of the Artmentioning

confidence: 99%

Application of Optimized Support Vector Machine Model in Tax Forecasting System

Xin

2022

Journal of Function Spaces

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Tax forecast has an important impact on financial budget and tax plan. The amount of tax data is greatly increased, the difficulty of tax forecast is improved, and the accuracy is always difficult to keep up with the development demand. Analyze the application of optimized support vector machine model in tax prediction system. Based on the simple analysis of the research situation of tax prediction and the research status of data mining algorithm in tax data classification and prediction, this paper constructs a tax prediction model. Aiming at the problem of too many influencing factors of tax prediction, this paper puts forward the use of principal component analysis to extract the main factors, reduce the dimension of tax data, and reduce the difficulty of analysis. Support vector machine is used to realize tax prediction, aiming at the problem of parameter optimization proposed to optimize the parameters. Finally, the prediction accuracy is evaluated by comparing the error between tax prediction value and real value. The results show that the algorithm used in this paper can optimize the parameters of support vector machine. The tax prediction results show that the predicted value is similar to the real value curve. After grid search optimization, the introduction of principal component analysis reduces the redundancy and improves the prediction accuracy.

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Section: State Of the Artmentioning

confidence: 99%

Application of Optimized Support Vector Machine Model in Tax Forecasting System

Xin

2022

Journal of Function Spaces

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“…Microscopically speaking, for mature listed companies, due to the poor management of normal business activities, some financial indicators will be abnormal. For newly listed companies, due to the strict listing conditions (especially on the main board), the company may whitewash the financial statements before listing in order to achieve the purpose of listing [4]. Once the company is listed, some financial indicators are bound to decline or rise, resulting in abnormalities.…”

Section: Introductionmentioning

confidence: 99%

Abnormal Data Monitoring and Analysis Based on Data Mining and Neural Network

Chen

2022

Journal of Sensors

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In order to solve the problems of low efficiency, high consumption of human and time resources, and low degree of intelligence in the current financial abnormal data detection system in computerized accounting, this paper proposes a financial abnormal data monitoring and analysis algorithm based on data mining and neural network. The analysis algorithm uses the method of data mining to process the original financial data, remove invalid information, retain valuable information, and standardize the data to solve the problem of large labor and time consumption. Then, neural network-related algorithms are used to identify the anomalies of standardized data, so as to realize the intelligent early warning of financial abnormal data. Compared with the financial audit algorithm in traditional accounting computerization, this algorithm has the advantages of high efficiency, low energy consumption, and high intelligence. The test results show that the classification accuracy of the proposed algorithm for abnormal data can reach more than 90%. It is proved that the algorithm is effective and improves the efficiency at the same time. The classification error rate of the classifier designed in this paper is 22.5%, and the accuracy rate is 77.5%. Both estimated and actual values represent the number of times, and there is no physical unit. The experiment shows that the main reason for the error rate is the delay of the inspection results of financial abnormalities. Through the example analysis, it can be concluded that the proposed intelligent analysis method of financial abnormal data based on deep learning has good effectiveness and accuracy and has a certain practical value.

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“…Many scholars have always selected financial ratio indicators [ 41 , 42 ] as fraud risk identification indicators when studying financial fraud identification, and some scholars have included nonfinancial indicators such as ownership structure [ 28 ] in the model indicator input. Few scholars have considered introducing textual information in annual reports into the indicators, and only a few scholars have attempted to do so.…”

Section: Literature Reviewmentioning

confidence: 99%

Financial Fraud Identification Based on Stacking Ensemble Learning Algorithm: Introducing MD&A Text Information

Zhang

Yong

Yong-jun

2022

Computational Intelligence and Neuroscience

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In recent years, there have been frequent incidents of financial fraud committed through various means. How to more efficiently identify financial fraud and maintain capital market order is a problem that scholars from all walks of life are discussing and urgently seeking to resolve. In this study, a financial fraud identification model is constructed based on the stacking ensemble learning algorithm, and the text of the management discussion and analysis (MD&A) chapter in annual reports is introduced based on financial and nonfinancial variables, using sentiment polarity, emotional tone, and text readability as text variables. The results show that when considering financial and nonfinancial variables and introducing text variables, the recognition effect of the stacking ensemble learning model constructed in this study is significantly better than the classification results of each single classifier model. In addition, the model recognition effect is better after adding text variables. Therefore, the model is expected to provide a new and more effective method of identifying financial fraud.

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Data mining of corporate financial fraud based on neural network model

Cited by 10 publications

References 16 publications

Application of Optimized Support Vector Machine Model in Tax Forecasting System

Application of Optimized Support Vector Machine Model in Tax Forecasting System

Abnormal Data Monitoring and Analysis Based on Data Mining and Neural Network

Financial Fraud Identification Based on Stacking Ensemble Learning Algorithm: Introducing MD&A Text Information

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